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Toward Intelligent Inertial Frequency Participation of Wind Farms for the Grid Frequency Control

机译：走向电网频率控制的智能惯性频率参与

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摘要

Evolving dynamics of modern power systems caused by high penetration of renewable energy sources increased the risk of failures and outages due to declining power system inertia. Large-scale wind farms must participate in frequency control that responds optimally in due time and adaptively in case of detecting power imbalance in the grid. Existing research studies have shown interest on stepwise inertial control (SIC) on wind turbines (WTs). However, the adequate power increment and time duration of WTs using SIC are the key questions that have not yet been fully addressed. This paper proposes an intelligent learning-based control system for WTs participation in frequency control, as well as for mitigating negative effects of the SIC. First, an appropriate optimization model for grid frequency control is defined. Then, the model is solved using lightning flash algorithm (LFA), imperialist competitive algorithm, and particle swarm optimization to control the WTs in a wind farm. The obtained dataset by LFA are applied to an artificial neural network that is trained with the Levenberg-Marquardt algorithm and LFA. The proposed control system optimally adjusts the power increment and duration time of participation for each WT in the farm. Analyses on a 100 MW wind farm has been integrated into the IEEE 9-bus system and experimental tests have proved the efficacy of the proposed approach.
机译：高效渗透率造成的现代电力系统的发展动态增加了由于电力系统惯性下降而导致故障和中断的风险。大型风电场必须参与频率控制，以便在由于检测电网中的功率不平衡的情况下，适当地响应最佳的频率控制。现有的研究研究表明了对风力涡轮机（WTS）的逐步惯性控制（SIC）的兴趣。然而，使用SIC的WTS的充分功率增量和持续时间是尚未完全解决的关键问题。本文提出了一种基于智能学习的控制系统，用于WTS参与频率控制，以及减轻SIC的负面影响。首先，定义了电网频率控制的适当优化模型。然后，使用雷击闪存算法（LFA），帝国主义竞争算法和粒子群优化来解决模型，以控制风电场中的WTS。 LFA所获得的数据集应用于用Levenberg-Marquardt算法和LFA接受培训的人工神经网络。所提出的控制系统最佳地调整农场中每个WT的功率增量和参与持续时间。 100 MW风电场的分析已集成到IEEE 9公交系统中，实验测试证明了所提出的方法的功效。

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